AI Token Group Performance

Long-term performance data across the AI token cohort indicates a meaningful shift in how the market is pricing the AI narrative. The “AI beta trade” phase—where most tokens rallied together on a shared story—has largely come to an end. The market is now entering a phase of deep dispersion, where returns are no longer evenly distributed but instead concentrated in a very small number of projects at very specific moments in time.Chart observations show that the majority of AI tokens have hovered around low or negative performance levels for extended periods. Sharp rallies do occur, but they tend to be short-lived, quickly absorbed by supply, and followed by a reversion toward equilibrium. This behavior reflects a capital environment dominated by event-driven flows and tactical positioning rather than long-term accumulation based on conviction. In other words, AI is no longer being priced as a continuous growth theme, but rather as a basket of high-risk assets that require clear catalysts to attract capital.

Notably, even tokens once viewed as representatives of “fundamental AI” have not been immune to this trend. Large-cap projects with strong narratives such as $TAO, $FET, and $INJ have shown weak average performance, compressed volatility, and a lack of sustained price expansion. This suggests that much of the long-term growth expectation was priced in early, while core fundamentals—such as real adoption, cash flows, or revenue generation—have not yet been strong enough to trigger a new re-rating cycle.

On the other hand, smaller tokens such as $BDX, $BEAT, or $KITE have shown periods of clear outperformance. However, it is important to emphasize that these moves have been largely driven by short-term catalysts or secondary narratives, with insufficient evidence to conclude the formation of a durable trend. From an institutional perspective, these should be treated as opportunity trades rather than core holdings.

From a capital allocation standpoint, the data clearly indicates that a “buy the entire AI sector” approach is no longer appropriate. The market is demanding a much higher level of selectivity, prioritizing projects that can clearly demonstrate three key elements: a real and defensible use case, an effective value-capture mechanism for the token, and sustainable on-chain demand or cash-flow generation. Projects that fail to meet these criteria are likely to remain range-bound for extended periods or continue to underperform, even if the AI narrative itself persists.

AI Tokens to Watch

$TAO remains one of the few representatives with a relatively coherent ecosystem and economic logic, but capital flows and signs of re-accumulation should be closely monitored before increasing exposure.

$FET benefits from the AI infrastructure narrative, though further confirmation is needed regarding real-world adoption and its ability to convert narrative strength into revenue.

$INJ is not a pure AI play but is closely linked to trading infrastructure and automation, making it more suitable as a hybrid exposure rather than a standalone AI thesis.

$BDX and $BEAT are better suited for catalyst-driven trading strategies and should not be assumed as long-term holdings by default.

$KITE and $VIRTUAL should be monitored primarily for volatility and short-term flow dynamics, with elevated risk in the absence of new narratives.

The AI token sector is entering a more unforgiving phase of maturity, where narrative alone is no longer sufficient to support prices. Returns will accrue to a small number of projects and during a limited number of windows. For professional investors and traders, disciplined selection, risk management, and a clear distinction between trading and holding will matter far more than broad exposure to a single overarching story.